Communication-efficient estimation of high-dimensional quantile regression
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1057-1075 |
| Journal / Publication | Analysis and Applications |
| Volume | 18 |
| Issue number | 6 |
| Online published | 14 Jul 2020 |
| Publication status | Published - Nov 2020 |
Link(s)
Abstract
Distributed estimation has received increasing attention in the last several years and is particularly useful in the big data setting. Both mean regression and quantile regression has been investigated recently. In this paper, we consider distributed quantile regression with high dimension using a lasso penalty for sparse modeling. We extend a previous communication-efficient approach resulting in a method for distributed quantile regression without the need to smooth the loss or the gradient of the loss. The method is simple to implement and we present some numerical studies with encouraging performances.
Research Area(s)
- Distributed estimator, divide and conquer, empirical processes, high-dimensional quantile regression
Citation Format(s)
Communication-efficient estimation of high-dimensional quantile regression. / Wang, Lei; Lian, Heng.
In: Analysis and Applications, Vol. 18, No. 6, 11.2020, p. 1057-1075.
In: Analysis and Applications, Vol. 18, No. 6, 11.2020, p. 1057-1075.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
